Create custom.py

custom.py

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+import tensorflow as tf
+
+inception_net = tf.keras.applications.MobileNetV2()
+
+import requests
+
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+def classify_image(inp):
+  inp = inp.reshape((-1, 224, 224, 3))
+  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+  prediction = inception_net.predict(inp).flatten()
+  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
+  return confidences
+
+import gradio as gr
+
+gr.Interface(fn=classify_image,
+             inputs=gr.Image(width=224, height=224),
+             outputs=gr.Label(num_top_classes=3),
+             examples=["banana.jpg", "car.jpg"]).launch()